One of the main ethical faults associated with the application of statistical learning to real-world problems is the acquisition of any biases that might be present in the training dataset. As [1]} discuss in their work, classical deep learning methods minimizing the expected risk of their hypothesis cannot distinguish between spurious correlations and true mechanisms. Therefore, naive statistical correspondences are drawn between phenomena that are not causally connected, creating unintended consequences with potential scientific or social impact. Although our model is still an expected-risk minimizing algorithm, we note that it could also be used as a paradigm for diagnosing dataset biases. For example, if two distinct populations of elements, sharing the same set of classes in a classification task, vary in their representation of different classes across different datasets, then the same individual placed in various datasets will produce, in turn, different content encodings, revealing the biases in the dataset. Our model is still very theoretical, but we believe it is a step towards a deeper study of the relationship between element and environment in the context of deep learning.
